{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: center\"><font color='#dc2624' face='微软雅黑' size = \"6\">零售商品销量预测</font></div>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" # 代码块显示所有执行结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: left\"><font color='black' face='微软雅黑' size = \"6\">引言</font><a name='top'></a></div>**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#需要的包\" data-toc-modified-id=\"需要的包-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>需要的包</a></span></li><li><span><a href=\"#数据集\" data-toc-modified-id=\"数据集-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>数据集</a></span><ul class=\"toc-item\"><li><span><a href=\"#商品信息\" data-toc-modified-id=\"商品信息-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>商品信息</a></span></li><li><span><a href=\"#提交格式\" data-toc-modified-id=\"提交格式-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>提交格式</a></span></li><li><span><a href=\"#店铺信息\" data-toc-modified-id=\"店铺信息-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>店铺信息</a></span></li><li><span><a href=\"#训练集\" data-toc-modified-id=\"训练集-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>训练集</a></span></li><li><span><a href=\"#测试集\" data-toc-modified-id=\"测试集-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>测试集</a></span></li></ul></li><li><span><a href=\"#数据清洗\" data-toc-modified-id=\"数据清洗-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>数据清洗</a></span><ul class=\"toc-item\"><li><span><a href=\"#店铺信息清洗\" data-toc-modified-id=\"店铺信息清洗-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>店铺信息清洗</a></span></li><li><span><a href=\"#商品信息清洗\" data-toc-modified-id=\"商品信息清洗-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>商品信息清洗</a></span></li><li><span><a href=\"#训练集清洗\" data-toc-modified-id=\"训练集清洗-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>训练集清洗</a></span></li></ul></li><li><span><a href=\"#按月聚合\" data-toc-modified-id=\"按月聚合-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>按月聚合</a></span></li><li><span><a href=\"#EDA\" data-toc-modified-id=\"EDA-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>EDA</a></span><ul class=\"toc-item\"><li><span><a href=\"#店铺销量占比\" data-toc-modified-id=\"店铺销量占比-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>店铺销量占比</a></span></li><li><span><a href=\"#店铺销量占比历史特征\" data-toc-modified-id=\"店铺销量占比历史特征-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>店铺销量占比历史特征</a></span></li><li><span><a href=\"#商品类别销量占比\" data-toc-modified-id=\"商品类别销量占比-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>商品类别销量占比</a></span></li><li><span><a href=\"#商品类别销量占比历史特征\" data-toc-modified-id=\"商品类别销量占比历史特征-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>商品类别销量占比历史特征</a></span></li><li><span><a href=\"#商品数量历史特征\" data-toc-modified-id=\"商品数量历史特征-5.5\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>商品数量历史特征</a></span></li><li><span><a href=\"#商品销量历史特征\" data-toc-modified-id=\"商品销量历史特征-5.6\"><span class=\"toc-item-num\">5.6&nbsp;&nbsp;</span>商品销量历史特征</a></span></li><li><span><a href=\"#‘商品销量’与‘商品数量’之比历史特征\" data-toc-modified-id=\"‘商品销量’与‘商品数量’之比历史特征-5.7\"><span class=\"toc-item-num\">5.7&nbsp;&nbsp;</span>‘商品销量’与‘商品数量’之比历史特征</a></span></li><li><span><a href=\"#商品总销量历史特征\" data-toc-modified-id=\"商品总销量历史特征-5.8\"><span class=\"toc-item-num\">5.8&nbsp;&nbsp;</span>商品总销量历史特征</a></span></li><li><span><a href=\"#小结\" data-toc-modified-id=\"小结-5.9\"><span class=\"toc-item-num\">5.9&nbsp;&nbsp;</span>小结</a></span></li></ul></li><li><span><a href=\"#总销量回归模型\" data-toc-modified-id=\"总销量回归模型-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>总销量回归模型</a></span></li><li><span><a href=\"#LightGBM\" data-toc-modified-id=\"LightGBM-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>LightGBM</a></span><ul class=\"toc-item\"><li><span><a href=\"#特征工程\" data-toc-modified-id=\"特征工程-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>特征工程</a></span></li><li><span><a href=\"#拟合模型\" data-toc-modified-id=\"拟合模型-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>拟合模型</a></span></li><li><span><a href=\"#评估模型\" data-toc-modified-id=\"评估模型-7.3\"><span class=\"toc-item-num\">7.3&nbsp;&nbsp;</span>评估模型</a></span></li><li><span><a href=\"#预测未来\" data-toc-modified-id=\"预测未来-7.4\"><span class=\"toc-item-num\">7.4&nbsp;&nbsp;</span>预测未来</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 需要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"SHXPgr\"></div>\n",
       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "                if(!window.letsPlotCallQueue) {\n",
       "                    window.letsPlotCallQueue = [];\n",
       "                }; \n",
       "                window.letsPlotCall = function(f) {\n",
       "                    window.letsPlotCallQueue.push(f);\n",
       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v2.2.0/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
       "                        document.getElementById(\"SHXPgr\").appendChild(div);\n",
       "                    };\n",
       "                    var e = document.getElementById(\"SHXPgr\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 可视化\n",
    "from lets_plot import LetsPlot, ggplot, aes, geom_line, geom_point, geom_boxplot, geom_bar, geom_smooth, geom_jitter\n",
    "from lets_plot import scale_color_brewer, scale_x_datetime, facet_wrap, ggsize, ylim\n",
    "LetsPlot.setup_html() # 默认开启JS交互模式\n",
    "# LetsPlot.setup_html(no_js=True) # 关闭JS交互模式\n",
    "\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import TreeMap\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# 建模\n",
    "import statsmodels.formula.api as smf\n",
    "import lightgbm as lgb\n",
    "\n",
    "# 评价模型\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "# 垃圾回收\n",
    "import gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据集\n",
    "**数据来源:** https://www.kaggle.com/c/competitive-data-science-predict-future-sales/data  \n",
    "数据来源于俄罗斯的零售业，任务是预测每个店铺每月每个商品的销量，需要注意的是，店铺和商品清单会随时间变化，因此需要创建一个泛化性能较好的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_category_name</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC - Гарнитуры/Наушники</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Аксессуары - PS2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Аксессуары - PS3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Аксессуары - PS4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Аксессуары - PSP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_category_name  item_category_id\n",
       "0  PC - Гарнитуры/Наушники                 0\n",
       "1         Аксессуары - PS2                 1\n",
       "2         Аксессуары - PS3                 2\n",
       "3         Аксессуары - PS4                 3\n",
       "4         Аксессуары - PSP                 4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "item_category_name    object\n",
       "item_category_id       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关于商品分类的补充信息\n",
    "item_categories = pd.read_csv('./predict future sales/item_categories.csv')\n",
    "item_categories.shape\n",
    "item_categories.head()\n",
    "item_categories.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22170, 3)"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>item_name</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                           item_name  item_id  \\\n",
       "0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D        0   \n",
       "1  !ABBYY FineReader 12 Professional Edition Full...        1   \n",
       "2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D        2   \n",
       "3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D        3   \n",
       "4        ***КОРОБКА (СТЕКЛО)                       D        4   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                40  \n",
       "3                40  \n",
       "4                40  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "item_name           object\n",
       "item_id              int64\n",
       "item_category_id     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关于商品名录的补充信息\n",
    "items_df = pd.read_csv('./predict future sales/items.csv')\n",
    "items_df.shape\n",
    "items_df.head()\n",
    "items_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**字段解释:**<br/>\n",
    "* `item_name`, 商品名称\n",
    "* `item_id`, 商品编号\n",
    "* `item_category_name`, 商品分类名称\n",
    "* `item_category_id`, 商品分类号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提交格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(214200, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  item_cnt_month\n",
       "0   0             0.5\n",
       "1   1             0.5\n",
       "2   2             0.5\n",
       "3   3             0.5\n",
       "4   4             0.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "ID                  int64\n",
       "item_cnt_month    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提交数据模板格式\n",
    "sample_submission = pd.read_csv('./predict future sales/sample_submission.csv')\n",
    "sample_submission.shape\n",
    "sample_submission.head()\n",
    "sample_submission.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**字段解释:**<br/>\n",
    "* `ID`, `(店铺, 商品)`的唯一化标识。\n",
    "* `item_cnt_month`, 月销量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 店铺信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_name</th>\n",
       "      <th>shop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Якутск Орджоникидзе, 56 фран</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!Якутск ТЦ \"Центральный\" фран</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Адыгея ТЦ \"Мега\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Волжский ТЦ \"Волга Молл\"</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        shop_name  shop_id\n",
       "0   !Якутск Орджоникидзе, 56 фран        0\n",
       "1   !Якутск ТЦ \"Центральный\" фран        1\n",
       "2                Адыгея ТЦ \"Мега\"        2\n",
       "3  Балашиха ТРК \"Октябрь-Киномир\"        3\n",
       "4        Волжский ТЦ \"Волга Молл\"        4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "shop_name    object\n",
       "shop_id       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 店铺信息\n",
    "shops_df = pd.read_csv('./predict future sales/shops.csv')\n",
    "shops_df.shape\n",
    "shops_df.head()\n",
    "shops_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**字段解释:**<br/>\n",
    "* `shop_name`, 店铺名称\n",
    "* `shop_id`, 店铺编号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 954 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2935849, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# 训练集，从2013年1月到2015年10月\n",
    "train_df = pd.read_csv('./predict future sales/sales_train.csv')\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "2  05.01.2013               0       25     2552      899.00          -1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "date               object\n",
       "date_block_num      int64\n",
       "shop_id             int64\n",
       "item_id             int64\n",
       "item_price        float64\n",
       "item_cnt_day      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**字段解释:**<br/>\n",
    "* `date`, 日期，格式为'dd/mm/yyyy'\n",
    "* `date_block_num`, 连续的月数，2013年1月为0，2015年10月为33\n",
    "* `shop_id`, 店铺编号\n",
    "* `item_id`, 商品编号\n",
    "* `item_price`, 商品实时价格。\n",
    "* `item_cnt_day`, 商品销量，任务是预测月销量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(214200, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  shop_id  item_id\n",
       "0   0        5     5037\n",
       "1   1        5     5320\n",
       "2   2        5     5233\n",
       "3   3        5     5232\n",
       "4   4        5     5268"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "ID         int64\n",
       "shop_id    int64\n",
       "item_id    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集，需要预测这些店铺的2015年11月销量\n",
    "test_df = pd.read_csv('./predict future sales/test.csv')\n",
    "test_df.shape\n",
    "test_df.head()\n",
    "test_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 店铺信息清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shop_name    False\n",
       "shop_id      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shops_df.isna().any(axis=0) # 检查缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shops_df['shop_id'].nunique() # 店铺数量\n",
    "shops_df['shop_id'].duplicated().any() # 是否重复\n",
    "# 检查训练集中店铺是否都在店铺数据中\n",
    "set(train_df['shop_id'].drop_duplicates()) - set(shops_df['shop_id'].drop_duplicates()) \n",
    "# 检查测试集中店铺是否都在店铺数据中\n",
    "set(test_df['shop_id'].drop_duplicates()) - set(shops_df['shop_id'].drop_duplicates()) \n",
    "# 检查测试集中店铺是否都在训练集中\n",
    "set(test_df['shop_id'].drop_duplicates()) - set(train_df['shop_id'].drop_duplicates())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果表明：  \n",
    "* 店铺数据非常干净，没有缺失值，没有重复值，\n",
    "* 训练集中所有店铺都在店铺数据中，\n",
    "* 测试集中所有店铺都在店铺数据中，\n",
    "* 测试集中所有店铺也都在训练集中"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品信息清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_category_name    False\n",
       "item_category_id      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "item_name           False\n",
       "item_id             False\n",
       "item_category_id    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_categories.isna().any(axis=0) # 检查缺失值\n",
    "items_df.isna().any(axis=0) # 检查缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "22170"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_categories['item_category_id'].nunique() # 商品分类数量\n",
    "item_categories['item_category_id'].duplicated().any() # 商品分类是否重复\n",
    "items_df['item_id'].nunique() # 商品数量\n",
    "items_df['item_id'].duplicated().any() # 商品是否重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_categories.shape[0]\n",
    "item_categories['item_category_id'].nunique()\n",
    "items_df['item_category_id'].nunique()\n",
    "# 检查商品数据中的商品分类是否都在商品分类数据中\n",
    "set(items_df['item_category_id'].drop_duplicates()) - set(item_categories['item_category_id']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查训练集中商品编号是否都在商品数据中\n",
    "set(train_df['item_id'].drop_duplicates()) - set(items_df['item_id'])\n",
    "# 检查测试集中商品编号是否都在商品数据中\n",
    "set(test_df['item_id'].drop_duplicates()) - set(items_df['item_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "363"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 检查测试集中商品编号是否都在训练集中\n",
    "list(set(test_df['item_id'].drop_duplicates()) - set(train_df['item_id'].drop_duplicates())).__len__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果表明：  \n",
    "* 商品数据和商品分类数据非常干净，没有缺失值，没有重复值，\n",
    "* 商品数据中商品分类编号都在商品分类数据中，\n",
    "* 训练集中所有商品编号都在商品数据中。\n",
    "* 测试集中所有商品编号都在商品数据中。\n",
    "* 测试集中有363个商品编号没有在训练集中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              False\n",
       "date_block_num    False\n",
       "shop_id           False\n",
       "item_id           False\n",
       "item_price        False\n",
       "item_cnt_day      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().any(axis=0) # 检查是否存在缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果表明：  \n",
    "* 训练集没有缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按月聚合\n",
    "因为任务是预测每月销量，并且每天的销量波动会很大，因此先按月进行聚合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1609124, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>ymonth</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>20</td>\n",
       "      <td>54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id     ymonth  date_block_num  shop_id  item_cnt_month  \\\n",
       "0        0 2014-09-01              20       54             1.0   \n",
       "1        1 2014-04-01              15       55             2.0   \n",
       "2        1 2014-07-01              18       55             1.0   \n",
       "3        1 2014-08-01              19       55             1.0   \n",
       "4        1 2014-09-01              20       55             1.0   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                76  \n",
       "3                76  \n",
       "4                76  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df2 = (train_df.pipe(lambda x: x.assign(date = pd.to_datetime(x['date'], format='%d.%m.%Y')))\n",
    "             .pipe(lambda df: df.assign(ymonth = df['date'].map(lambda x: pd.offsets.MonthBegin().rollback(x)))) # 偏移到月初\n",
    "             .set_index(['ymonth', 'date_block_num', 'shop_id', 'item_id'])\n",
    "             .groupby(level=['ymonth', 'date_block_num', 'shop_id', 'item_id'])['item_cnt_day']\n",
    "             .sum()\n",
    "             .reset_index()\n",
    "             .rename({'item_cnt_day': 'item_cnt_month'}, axis=1)\n",
    "             .set_index('item_id')\n",
    "             .join(items_df.set_index('item_id')[['item_category_id']], how='left')\n",
    "             .reset_index()\n",
    "            )\n",
    "train_df2.shape\n",
    "train_df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果仍然有160W行，相比原数据293W行，聚合后似乎减少不到1半，所以可以看出很多商品一个月都是零销量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 店铺销量占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shop_propotion = (train_df2.groupby(by='shop_id')['item_cnt_month']\n",
    "                  .sum()\n",
    "                  .reset_index()\n",
    "                  .eval(\"shop_id = shop_id.astype('string')\", engine='python')\n",
    "                  .assign(shop = 'shop')\n",
    "                  .eval(\"shop_id = shop.str.cat(shop_id, sep='-', join='left')\", engine='python')\n",
    "                  .set_index('shop_id')\n",
    "                  .drop('shop', axis=1)\n",
    "                  .to_dict()['item_cnt_month']\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'westeros':'https://assets.pyecharts.org/assets/themes/westeros'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"4d53efe72a764aecaba9746d2964d668\" style=\"width:950px; height:600px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'westeros'], function(echarts) {\n",
       "                var chart_4d53efe72a764aecaba9746d2964d668 = echarts.init(\n",
       "                    document.getElementById('4d53efe72a764aecaba9746d2964d668'), 'westeros', {renderer: 'canvas'});\n",
       "                var option_4d53efe72a764aecaba9746d2964d668 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"treemap\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"value\": 11705.0,\n",
       "                    \"name\": \"shop-0\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 6311.0,\n",
       "                    \"name\": \"shop-1\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 30620.0,\n",
       "                    \"name\": \"shop-2\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 28355.0,\n",
       "                    \"name\": \"shop-3\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 43942.0,\n",
       "                    \"name\": \"shop-4\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 42762.0,\n",
       "                    \"name\": \"shop-5\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 100489.0,\n",
       "                    \"name\": \"shop-6\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 67058.0,\n",
       "                    \"name\": \"shop-7\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 3595.0,\n",
       "                    \"name\": \"shop-8\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 15866.0,\n",
       "                    \"name\": \"shop-9\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 24523.0,\n",
       "                    \"name\": \"shop-10\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 572.0,\n",
       "                    \"name\": \"shop-11\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 73478.0,\n",
       "                    \"name\": \"shop-12\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 19763.0,\n",
       "                    \"name\": \"shop-13\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 46375.0,\n",
       "                    \"name\": \"shop-14\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 71201.0,\n",
       "                    \"name\": \"shop-15\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 61633.0,\n",
       "                    \"name\": \"shop-16\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 25838.0,\n",
       "                    \"name\": \"shop-17\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 65486.0,\n",
       "                    \"name\": \"shop-18\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 73455.0,\n",
       "                    \"name\": \"shop-19\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 5872.0,\n",
       "                    \"name\": \"shop-20\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 68560.0,\n",
       "                    \"name\": \"shop-21\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 60230.0,\n",
       "                    \"name\": \"shop-22\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 7705.0,\n",
       "                    \"name\": \"shop-23\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 63886.0,\n",
       "                    \"name\": \"shop-24\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 241920.0,\n",
       "                    \"name\": \"shop-25\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 67890.0,\n",
       "                    \"name\": \"shop-26\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 136657.0,\n",
       "                    \"name\": \"shop-27\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 184557.0,\n",
       "                    \"name\": \"shop-28\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 58713.0,\n",
       "                    \"name\": \"shop-29\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 60828.0,\n",
       "                    \"name\": \"shop-30\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 310777.0,\n",
       "                    \"name\": \"shop-31\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 8781.0,\n",
       "                    \"name\": \"shop-32\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 5482.0,\n",
       "                    \"name\": \"shop-33\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 6451.0,\n",
       "                    \"name\": \"shop-34\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 69016.0,\n",
       "                    \"name\": \"shop-35\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 330.0,\n",
       "                    \"name\": \"shop-36\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 46256.0,\n",
       "                    \"name\": \"shop-37\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 53886.0,\n",
       "                    \"name\": \"shop-38\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 16617.0,\n",
       "                    \"name\": \"shop-39\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 4943.0,\n",
       "                    \"name\": \"shop-40\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 49324.0,\n",
       "                    \"name\": \"shop-41\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 144934.0,\n",
       "                    \"name\": \"shop-42\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 50608.0,\n",
       "                    \"name\": \"shop-43\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 44938.0,\n",
       "                    \"name\": \"shop-44\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 41895.0,\n",
       "                    \"name\": \"shop-45\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 78990.0,\n",
       "                    \"name\": \"shop-46\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 67637.0,\n",
       "                    \"name\": \"shop-47\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 24909.0,\n",
       "                    \"name\": \"shop-48\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 17090.0,\n",
       "                    \"name\": \"shop-49\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 76238.0,\n",
       "                    \"name\": \"shop-50\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 48767.0,\n",
       "                    \"name\": \"shop-51\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 49744.0,\n",
       "                    \"name\": \"shop-52\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 61657.0,\n",
       "                    \"name\": \"shop-53\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 185790.0,\n",
       "                    \"name\": \"shop-54\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 63388.0,\n",
       "                    \"name\": \"shop-55\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 78079.0,\n",
       "                    \"name\": \"shop-56\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 141107.0,\n",
       "                    \"name\": \"shop-57\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 81734.0,\n",
       "                    \"name\": \"shop-58\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 48993.0,\n",
       "                    \"name\": \"shop-59\"\n",
       "                }\n",
       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Propotion of Shops\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_4d53efe72a764aecaba9746d2964d668.setOption(option_4d53efe72a764aecaba9746d2964d668);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x29e04a78188>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_treemap = [{\"value\": v, \"name\": k} for (k, v) in shop_propotion.items()]\n",
    "\n",
    "(\n",
    "    TreeMap(init_opts=opts.InitOpts(width=\"950px\", height=\"600px\", theme=\"westeros\"))\n",
    "    .add(\"\", data_for_treemap)\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"Propotion of Shops\"))\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "店铺在销量中的分布非常均匀, 没有一家独大的情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 店铺销量占比历史特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>shop_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>25</td>\n",
       "      <td>6.248146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>28</td>\n",
       "      <td>5.367397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>31</td>\n",
       "      <td>7.737357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>54</td>\n",
       "      <td>7.138783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>others</td>\n",
       "      <td>73.508317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth shop_id  shop_prop\n",
       "0 2013-01-01      25   6.248146\n",
       "1 2013-01-01      28   5.367397\n",
       "2 2013-01-01      31   7.737357\n",
       "3 2013-01-01      54   7.138783\n",
       "4 2013-01-01  others  73.508317"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_propotion_h = (train_df2.set_index(['ymonth', 'shop_id'])\n",
    "                    .groupby(level=['ymonth', 'shop_id'])['item_cnt_month']\n",
    "                    .sum()\n",
    "                    .reset_index()\n",
    "                    .eval(\"shop_id = shop_id.astype('string')\", engine='python')\n",
    "                    .set_index('ymonth')\n",
    "                    .groupby(level='ymonth')\n",
    "                    .apply(lambda x: x.eval(\"shop_prop = item_cnt_month/item_cnt_month.sum()\", engine='python'))\n",
    "                    .pipe(lambda x: x.assign(shop_id = x['shop_id'].mask(x['shop_prop'] < 0.05, 'others')))\n",
    "                    .reset_index()\n",
    "                    .set_index(['ymonth', 'shop_id'])\n",
    "                    .groupby(level=['ymonth', 'shop_id'])['shop_prop']\n",
    "                    .sum()\n",
    "                    .reset_index()\n",
    "                    .eval(\"shop_prop = shop_prop * 100\")\n",
    "                   )\n",
    "shop_propotion_h['shop_id'].nunique() # 超过5%的类别数量\n",
    "shop_propotion_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"IPja6c\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
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       "\"shop_prop\":[6.24814609177131,5.367397074818032,7.7373572966025,7.138782619277603,73.50831691753056,6.194082285892732,8.712623936294793,6.304161136700757,78.78913264111172,6.614019110790936,8.610729771241386,6.448192902094576,78.32705821587311,7.339304039555929,8.97565071368598,6.274839070808843,5.1245452001119505,72.2856609758373,6.413012994297467,5.331401327474993,8.809011872487615,6.549499859773768,72.89707394596616,6.761790063885277,5.627646932150805,8.033115065281024,6.461106547244,73.1163413914389,6.2932818083887625,5.761503342851769,8.122018364310996,6.177008703383889,73.64618778106458,6.21273674884868,6.002825422416614,7.887238508751626,6.2191218842534575,73.67807743572962,6.938319383193832,5.06480064800648,8.40308403084031,5.870308703087031,73.72348723487234,6.5516186951646915,8.431798402082467,5.2343952140880186,5.116785974706173,74.66540171395864,6.639540339514957,9.007068741394827,6.13649824242937,78.21689267666085,6.600778872271492,8.90030653096399,5.4842861973797605,5.172300945773473,73.84232745361128,5.846927689715054,5.944447771152875,8.5689355768655,7.0128914704146315,72.62679749185195,6.095526361373727,5.012444501171515,8.868872336739997,5.766408052002516,74.25674874871224,5.920362195026757,5.118086333555947,8.363617440176242,5.963728457808963,74.63420557343208,6.150834748746841,9.21434193628568,5.773851443721778,78.8609718712457,5.8758564270375295,5.012782493097453,8.00286327845383,5.719398711524696,75.38909908988649,6.57401800285336,5.851440536185325,7.904217430128606,6.19220150057991,73.4781225302528,6.156879929886065,7.730061349693251,5.683610867659947,80.42944785276075,6.040634339618968,5.648309498544601,8.029516846603908,6.026031678040518,74.25550763719201,6.207160712845738,8.651520038706556,5.074187565518909,80.06713168292879,5.934538548900598,7.872689020870957,5.729738787213048,80.4630336430154,6.517883660740803,8.371165514022657,5.421528278671136,79.6894225465654,7.263192201712541,8.657521258629373,84.07928653965809,5.957412296906399,5.100431644303467,6.198916834127835,7.943516774652837,5.445566859810221,5.7934054843157226,63.56075010588352,6.610608327988789,5.028623007672391,8.793558041758711,5.013183211002636,74.55402741157748,7.324359255736826,5.046699344014437,8.893603531104445,5.039383519886849,5.639281098349063,68.05667325090839,7.882868413275598,9.432459172266693,5.972220437637324,76.71245197682039,5.462341794038315,7.900961338958434,5.2368766858012314,5.234110242755377,8.751642575558476,6.007331074071512,61.40673628881665,7.943662850547463,5.23598590011542,6.115668964656705,9.607885953145958,5.64307327572761,65.45372305580685,7.4002563818506975,5.716365708136167,9.475050247677528,5.662557171570101,71.74577049076551,7.074864934396707,5.6735120083536374,8.647225290939634,5.799119236065921,72.8052785302441,7.159232870694507,8.09000178466016,8.93016487514243,5.816619304531664,9.427124088793706,60.57685707617754,5.88409142085116,8.791657284395406,8.601666291375816,6.3203670344517,70.40221796892592]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"shop_prop\",\n",
       "\"group\":\"shop_id\",\n",
       "\"fill\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":900,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"stat\":\"identity\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"IPja6c\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e5e2ce448>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(shop_propotion_h, aes(x='ymonth', y='shop_prop', group='shop_id', fill='shop_id')) \\\n",
    "+ geom_bar(stat='identity') \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(900, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "店铺在销量中的分布非常均匀, 历史数据中没有1家店铺销量占比超过5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品类别销量占比\n",
    "为了提高预测准确性，还需要分析每种商品类别在总销量中的占比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_propotion = (train_df2.reindex(['ymonth', 'item_category_id', 'item_cnt_month'], axis=1)\n",
    "                      .set_index(['ymonth', 'item_category_id'])\n",
    "                      .groupby(level=['ymonth', 'item_category_id'])\n",
    "                      .sum()\n",
    "                      .reset_index().groupby(by='item_category_id')['item_cnt_month']\n",
    "                      .sum()\n",
    "                      .reset_index()\n",
    "                      .eval(\"item_category_id = item_category_id.astype('string')\", engine='python')\n",
    "                      .assign(category='category')\n",
    "                      .eval(\"item_category_id = category.str.cat(item_category_id, sep='-', join='left')\", engine='python')\n",
    "                      .set_index('item_category_id')\n",
    "                      .drop('category', axis=1)\n",
    "                      .to_dict()['item_cnt_month']\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                    \"name\": \"category-65\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 5910.0,\n",
       "                    \"name\": \"category-66\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 44303.0,\n",
       "                    \"name\": \"category-67\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 129.0,\n",
       "                    \"name\": \"category-68\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 31895.0,\n",
       "                    \"name\": \"category-69\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 45067.0,\n",
       "                    \"name\": \"category-70\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 187998.0,\n",
       "                    \"name\": \"category-71\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 51621.0,\n",
       "                    \"name\": \"category-72\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 14573.0,\n",
       "                    \"name\": \"category-73\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 59.0,\n",
       "                    \"name\": \"category-74\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 48224.0,\n",
       "                    \"name\": \"category-75\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 5647.0,\n",
       "                    \"name\": \"category-76\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 3900.0,\n",
       "                    \"name\": \"category-77\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 2551.0,\n",
       "                    \"name\": \"category-78\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 15830.0,\n",
       "                    \"name\": \"category-79\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 6136.0,\n",
       "                    \"name\": \"category-80\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 965.0,\n",
       "                    \"name\": \"category-81\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 10437.0,\n",
       "                    \"name\": \"category-82\"\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 12032.0,\n",
       "                    \"name\": \"category-83\"\n",
       "                }\n",
       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Propotion of Categories\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_9e00e24d5b7042419b82a461791cf832.setOption(option_9e00e24d5b7042419b82a461791cf832);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x29e04caa1c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_treemap = [{\"value\": v, \"name\": k} for (k, v) in category_propotion.items()]\n",
    "\n",
    "(\n",
    "    TreeMap(init_opts=opts.InitOpts(width=\"950px\", height=\"600px\", theme=\"westeros\"))\n",
    "    .add(\"\", data_for_treemap)\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"Propotion of Categories\"))\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品类别销量占比历史特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提取销量占比超过10%的类别的历史曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>category_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>30</td>\n",
       "      <td>16.896995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>40</td>\n",
       "      <td>25.470988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>others</td>\n",
       "      <td>57.632017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>30</td>\n",
       "      <td>16.368179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>40</td>\n",
       "      <td>24.708408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth item_category_id  category_prop\n",
       "0 2013-01-01               30      16.896995\n",
       "1 2013-01-01               40      25.470988\n",
       "2 2013-01-01           others      57.632017\n",
       "3 2013-02-01               30      16.368179\n",
       "4 2013-02-01               40      24.708408"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_propotion_h = (train_df2.reindex(['ymonth', 'item_category_id', 'item_cnt_month'], axis=1)\n",
    "                        .set_index(['ymonth', 'item_category_id'])\n",
    "                        .groupby(level=['ymonth', 'item_category_id'])\n",
    "                        .sum()\n",
    "                        .reset_index()\n",
    "                        .eval(\"item_category_id = item_category_id.astype('string')\", engine='python')\n",
    "                        .set_index('ymonth')\n",
    "                        .groupby(level='ymonth')\n",
    "                        .apply(lambda x: x.eval(\"category_prop = item_cnt_month/item_cnt_month.sum()\", engine='python'))\n",
    "                        .pipe(lambda x: x.assign(item_category_id = x['item_category_id'].mask(x['category_prop'] < 0.1, 'others')))\n",
    "                        .reset_index()\n",
    "                        .set_index(['ymonth', 'item_category_id'])\n",
    "                        .groupby(level=['ymonth', 'item_category_id'])['category_prop']\n",
    "                        .sum()\n",
    "                        .reset_index()\n",
    "                        .eval(\"category_prop = category_prop * 100\")\n",
    "                       )\n",
    "category_propotion_h['item_category_id'].nunique() # 超过10%的类别数量\n",
    "category_propotion_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"eR5V1n\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"ymonth\":[1.3569984E12,1.3569984E12,1.3569984E12,1.3596768E12,1.3596768E12,1.3596768E12,1.362096E12,1.362096E12,1.362096E12,1.3647744E12,1.3647744E12,1.3647744E12,1.3647744E12,1.3673664E12,1.3673664E12,1.3673664E12,1.3673664E12,1.3700448E12,1.3700448E12,1.3700448E12,1.3700448E12,1.3726368E12,1.3726368E12,1.3726368E12,1.3726368E12,1.3753152E12,1.3753152E12,1.3753152E12,1.3753152E12,1.3779936E12,1.3779936E12,1.3779936E12,1.3779936E12,1.3805856E12,1.3805856E12,1.3805856E12,1.3805856E12,1.383264E12,1.383264E12,1.383264E12,1.385856E12,1.385856E12,1.385856E12,1.3885344E12,1.3885344E12,1.3885344E12,1.3912128E12,1.3912128E12,1.3912128E12,1.393632E12,1.393632E12,1.393632E12,1.393632E12,1.3963104E12,1.3963104E12,1.3963104E12,1.3989024E12,1.3989024E12,1.3989024E12,1.4015808E12,1.4015808E12,1.4015808E12,1.4015808E12,1.4041728E12,1.4041728E12,1.4041728E12,1.4041728E12,1.4068512E12,1.4068512E12,1.4068512E12,1.4068512E12,1.4095296E12,1.4095296E12,1.4095296E12,1.4121216E12,1.4121216E12,1.4121216E12,1.4148E12,1.4148E12,1.4148E12,1.417392E12,1.417392E12,1.4200704E12,1.4200704E12,1.4227488E12,1.4227488E12,1.425168E12,1.425168E12,1.425168E12,1.4278464E12,1.4278464E12,1.4278464E12,1.4304384E12,1.4304384E12,1.4331168E12,1.4331168E12,1.4357088E12,1.4357088E12,1.4357088E12,1.4383872E12,1.4383872E12,1.4410656E12,1.4436576E12,1.4436576E12],\n",
       "\"item_category_id\":[\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"19\",\"30\",\"40\",\"others\",\"19\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"55\",\"others\",\"30\",\"40\",\"others\",\"30\",\"40\",\"others\",\"20\",\"40\",\"others\",\"40\",\"others\",\"40\",\"others\",\"40\",\"others\",\"40\",\"55\",\"others\",\"30\",\"40\",\"others\",\"40\",\"others\",\"40\",\"others\",\"40\",\"55\",\"others\",\"40\",\"others\",\"others\",\"40\",\"others\"],\n",
       "\"category_prop\":[16.896994957369618,25.470987762304247,57.63201728032613,16.368178624404717,24.708408150519166,58.92341322507612,18.380204156529068,24.654415462614345,56.96538038085659,15.43800727679821,25.678701371396585,10.95158130422614,47.93171004757906,16.100775918481816,23.81041413480415,11.412545573525287,48.676264373188744,15.270256258922805,20.73041369904531,10.859699635510962,53.13963040652092,14.777798676538481,21.20787237316827,12.112921703742968,51.90140724655028,15.436064841050035,19.25916466466067,11.27854355061417,54.02622694367513,14.199141991419914,15.384903849038489,17.51117511175112,52.90477904779047,11.065461302639935,14.151527744019571,18.953120957182396,55.8298899961581,12.917567245344552,19.562491827488866,67.51994092716657,10.956572962005433,15.598171722791287,73.44525531520328,12.73150326350097,18.875268394083783,68.39322834241524,12.037889631405726,16.836088141712327,71.12602222688194,12.69070314058475,18.444538886527837,10.002862173343626,58.86189579954378,11.138613861386139,18.973445461700983,69.88794067691289,11.866243992228243,15.344104714183453,72.78965129358829,11.952293464984757,16.487904012152438,11.0531771854376,60.5066253374252,11.454864154250657,15.832602979842244,12.457274320771253,60.255258545135845,11.820367792369622,15.401913922177549,10.883850429805006,61.89386785564782,10.210870091121684,12.849770179824208,76.9393597290541,13.642456852413845,12.696654316620432,73.66088883096572,10.89057660486232,12.571598285884,76.53782510925367,11.185446357144974,88.81455364285502,13.615268854024926,86.38473114597508,14.370887669540844,85.62911233045915,15.95337381422684,10.828639012851465,73.2179871729217,18.95357652228661,13.726598738227095,67.31982473948631,12.736703783110864,87.26329621688915,14.511651121439936,85.48834887856007,14.691313086552613,10.245778403785588,75.0629085096618,12.883064211020143,87.11693578897986,100.0,10.80837649178113,89.19162350821887]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"category_prop\",\n",
       "\"group\":\"item_category_id\",\n",
       "\"fill\":\"item_category_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
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       "\"ggsize\":{\n",
       "\"width\":900,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
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       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"stat\":\"identity\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
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       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"eR5V1n\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e23fc5648>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(category_propotion_h, aes(x='ymonth', y='category_prop', group='item_category_id', fill='item_category_id')) \\\n",
    "+ geom_bar(stat='identity') \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(900, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "占比最高的类别编号为40，最高也就25%，最开始前3种商品销量占比就能超过40%, 但商品类别随时间在销量中的分布更加均匀，因此不需要对单个类别进行建模。如果某个或某几个类别在总体销量中的比例比较高，且其随时间变化区域与总体趋势有差异，那么就需要分开建模，然后再叠加，这样更加准确。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品数量历史特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1586, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  shop_id  item_id\n",
       "0 2013-01-01        0     2385\n",
       "1 2013-01-01        1     1535\n",
       "2 2013-01-01        2      728\n",
       "3 2013-01-01        3      544\n",
       "4 2013-01-01        4     1062"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_num = (train_df2.reindex(['ymonth', 'shop_id', 'item_id'], axis=1)\n",
    "             .set_index(['ymonth', 'shop_id'])\n",
    "             .groupby(level=['ymonth', 'shop_id'])\n",
    "             .count()\n",
    "             .reset_index()\n",
    "            )\n",
    "\n",
    "items_num.shape\n",
    "items_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"0WMN1H\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"item_id\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"0WMN1H\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04d4fb08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(items_num, aes(x='ymonth', y='item_id', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出, 大部分店铺的商品数量都随时间而减少, 但存在季节性特征，周期为12个月, 每年12月会有回升。  \n",
    "因为店铺数据是变化的，下面去掉测试集中不存在的店铺，绘图试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"hmBccl\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"item_id\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"hmBccl\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04d2fa88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(items_num[items_num['shop_id'].isin(test_df['shop_id'])], \n",
    "       aes(x='ymonth', y='item_id', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "放大y轴$300\\sim 1700$范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"VZRxSt\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"item_id\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "},{\n",
       "\"aesthetic\":\"y\",\n",
       "\"limits\":[300,1700]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"VZRxSt\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04d26888>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(items_num[items_num['shop_id'].isin(test_df['shop_id'])], \n",
    "       aes(x='ymonth', y='item_id', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ylim(300, 1700) \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品销量历史特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1586, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>5578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>2114.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  shop_id  item_cnt_month\n",
       "0 2013-01-01        0          5578.0\n",
       "1 2013-01-01        1          2947.0\n",
       "2 2013-01-01        2          1146.0\n",
       "3 2013-01-01        3           767.0\n",
       "4 2013-01-01        4          2114.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_num_h = (train_df2.reindex(['ymonth', 'shop_id', 'item_cnt_month'], axis=1)\n",
    "               .set_index(['ymonth', 'shop_id'])\n",
    "               .groupby(level=['ymonth', 'shop_id'])\n",
    "               .sum()\n",
    "               .reset_index()\n",
    "              )\n",
    "\n",
    "sales_num_h.shape\n",
    "sales_num_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"4ECLVD\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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   ],
   "source": [
    "ggplot(sales_num_h, aes(x='ymonth', y='item_cnt_month', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，销量的变化趋势与商品数量的趋势大致相同，都是随时间而下降，只有在每年12月有回升。  \n",
    "因为店铺数据是变化的，下面去掉测试集中不存在的店铺，绘图试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"mkOB7d\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"item_cnt_month\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"mkOB7d\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04cd2048>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(sales_num_h[sales_num_h['shop_id'].isin(test_df['shop_id'])], \n",
    "       aes(x='ymonth', y='item_cnt_month', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，大部分店铺商品销量也随时间而减少，但存在季节性特征，周期为12个月, 每年12月会有回升。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "放大y轴$0\\sim 4000$范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"NwRplI\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ggplot(sales_num_h[sales_num_h['shop_id'].isin(test_df['shop_id'])], \n",
    "       aes(x='ymonth', y='item_cnt_month', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ylim(0, 4000) \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ‘商品销量’与‘商品数量’之比历史特征\n",
    "从前面的小节来看，虽然商品销量在每年12月份会大幅度增加，但是商品数量也会大幅度增加，是否是由于数量的增加，导致销量的增加。\n",
    "还是由于节假日效应，如圣诞节，导致销量大幅度增加。可以通过销量与数量的比值来分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1586, 5)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>sales_items_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2385</td>\n",
       "      <td>5578.0</td>\n",
       "      <td>2.338784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1535</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>1.919870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>728</td>\n",
       "      <td>1146.0</td>\n",
       "      <td>1.574176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>544</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1.409926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>1062</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>1.990584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  shop_id  item_num  item_cnt_month  sales_items_ratio\n",
       "0 2013-01-01        0      2385          5578.0           2.338784\n",
       "1 2013-01-01        1      1535          2947.0           1.919870\n",
       "2 2013-01-01        2       728          1146.0           1.574176\n",
       "3 2013-01-01        3       544           767.0           1.409926\n",
       "4 2013-01-01        4      1062          2114.0           1.990584"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_items_ratio_h = (train_df2.reindex(['ymonth', 'shop_id', 'item_id', 'item_cnt_month'], axis=1)\n",
    "                       .set_index(['ymonth', 'shop_id'])\n",
    "                       .groupby(level=['ymonth', 'shop_id'])\n",
    "                       .agg(\n",
    "                           item_num = ('item_id', 'count'), \n",
    "                           item_cnt_month = ('item_cnt_month', 'sum')\n",
    "                       )\n",
    "                       .reset_index()\n",
    "                       .eval(\"sales_items_ratio = item_cnt_month / item_num\")\n",
    "                      )\n",
    "sales_items_ratio_h.shape\n",
    "sales_items_ratio_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"data\":{\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"sales_items_ratio\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"f4oKzb\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04ccd408>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(sales_items_ratio_h, aes(x='ymonth', y='sales_items_ratio', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "放大y轴$1\\sim 4.5$范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"sales_items_ratio\",\n",
       "\"group\":\"shop_id\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "},{\n",
       "\"aesthetic\":\"y\",\n",
       "\"limits\":[1,4.5]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.3333333333333333,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"IhVUz3\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04cd01c8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(sales_items_ratio_h, aes(x='ymonth', y='sales_items_ratio', group='shop_id')) \\\n",
    "+ geom_line(color='#4169E1', alpha=1/3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ylim(1, 4.5) \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，'商品销量'与'商品数量'之比仍然存在季节性特征，周期仍然是12个月，每年12月会有1个高峰。  \n",
    "结合'商品销量历史特征图'和'商品数量历史特征图', 可以得出结论，虽然商品数量会在一定程度上影响销量，但是没有季节性周期影响大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品总销量历史特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>131479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>128090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2</td>\n",
       "      <td>147142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>107190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>4</td>\n",
       "      <td>106970.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  date_block_num  item_cnt_month\n",
       "0 2013-01-01               0        131479.0\n",
       "1 2013-02-01               1        128090.0\n",
       "2 2013-03-01               2        147142.0\n",
       "3 2013-04-01               3        107190.0\n",
       "4 2013-05-01               4        106970.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_num_all_h = (train_df2.reindex(['ymonth', 'date_block_num', 'item_cnt_month'], axis=1)\n",
    "                   .set_index(['ymonth', 'date_block_num'])\n",
    "                   .groupby(level=['ymonth', 'date_block_num'])\n",
    "                   .sum()\n",
    "                   .reset_index()\n",
    "                  )\n",
    "\n",
    "sales_num_all_h.shape\n",
    "sales_num_all_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"utiL9P\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "\"ymonth\":[1.3569984E12,1.3596768E12,1.362096E12,1.3647744E12,1.3673664E12,1.3700448E12,1.3726368E12,1.3753152E12,1.3779936E12,1.3805856E12,1.383264E12,1.385856E12,1.3885344E12,1.3912128E12,1.393632E12,1.3963104E12,1.3989024E12,1.4015808E12,1.4041728E12,1.4068512E12,1.4095296E12,1.4121216E12,1.4148E12,1.417392E12,1.4200704E12,1.4227488E12,1.425168E12,1.4278464E12,1.4304384E12,1.4331168E12,1.4357088E12,1.4383872E12,1.4410656E12,1.4436576E12],\n",
       "\"item_cnt_month\":[131479.0,128090.0,147142.0,107190.0,106970.0,125381.0,116966.0,125291.0,133332.0,127541.0,130009.0,183342.0,116899.0,109687.0,115297.0,96556.0,97790.0,97429.0,91280.0,102721.0,99208.0,107422.0,117845.0,168755.0,110971.0,84198.0,82014.0,77827.0,72295.0,64114.0,63187.0,66079.0,72843.0,71056.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"ymonth\",\n",
       "\"y\":\"item_cnt_month\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%Y-%m\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#4169E1\",\n",
       "\"alpha\":0.7,\n",
       "\"size\":1,\n",
       "\"data\":{\n",
       "}\n",
       "},{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#FF00FF\",\n",
       "\"size\":3,\n",
       "\"data\":{\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"utiL9P\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x29e04cf0888>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(sales_num_all_h, aes(x='ymonth', y='item_cnt_month')) \\\n",
    "+ geom_line(color='#4169E1', alpha=0.7, size=1) \\\n",
    "+ geom_point(color='#FF00FF', size=3) \\\n",
    "+ scale_x_datetime(format='%Y-%m') \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从总销量历史曲线来看，季节性特征更加明显，周期12个月，每年12月为高峰。销量随时间总体成下降趋势。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 小结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从EDA的几幅图可以得出:  \n",
    "* 没有一家独大的店铺，历史数据中没有店铺月销量占比超过总体销量的5%。\n",
    "* 销量在商品类别中的分布比较均匀，商品类别对于总体销量的影响也在下降。\n",
    "* 大部分店铺的商品数量都随时间而减少, 但存在季节性特征，周期为12个月, 每年12月会有回升。\n",
    "* 大部分店铺商品销量也随时间而减少，但存在季节性特征，周期为12个月, 每年12月会有回升。\n",
    "* '商品数量'对'商品销量'有影响，但是影响没有季节性特征大。商品数量增加的诱因是因为商品销量增加，增加数量能够提高利润。反过来则数据支撑性不足。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总销量回归模型\n",
    "&emsp;&emsp;如果是预测总体销量，则非常简单。基于前面的EDA分析结果，因为销量具有季节性特征，周期为12个月，其它季节性特征并不明显，因此不使用ARIMA模型。而是使用回归的方式，因为具有季节性特征，因此仅仅使用日期作为特征是不够的，这里我们增加了个特征，就是月份，月份为连续变量。\n",
    "考虑到12月为每年高峰，再设1个哑变量，即月份是否为12月。观察每年从1月到12月销量变化趋势是先下降再上升，因此可以通过余弦函数进行模拟。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>month</th>\n",
       "      <th>mth12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>131479.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>128090.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2</td>\n",
       "      <td>147142.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>107190.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>4</td>\n",
       "      <td>106970.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  date_block_num  item_cnt_month  month  mth12\n",
       "0 2013-01-01               0        131479.0      1      0\n",
       "1 2013-02-01               1        128090.0      2      0\n",
       "2 2013-03-01               2        147142.0      3      0\n",
       "3 2013-04-01               3        107190.0      4      0\n",
       "4 2013-05-01               4        106970.0      5      0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义1个函数，添加特征\n",
    "def add_feature_month(df):\n",
    "    return (df.eval(\"month = ymonth.dt.month\", engine='python')\n",
    "            .pipe(lambda x: x.assign(mth12 = np.where(x['month'] == 12, 1, 0)))\n",
    "           )\n",
    "sales_num_all_tr = sales_num_all_h.query(\"ymonth < '2015-09-01'\").pipe(add_feature_month)\n",
    "sales_num_all_va = sales_num_all_h.query(\"ymonth >= '2015-09-01'\").pipe(add_feature_month)\n",
    "sales_num_all_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总销量鲁棒回归模型RMSE: 70110329.97642328\n",
      "总销量鲁棒回归模型MAE: 7045.569245686827\n",
      "总销量鲁棒回归模型R方: 0.9067698922083129\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>month</th>\n",
       "      <th>mth12</th>\n",
       "      <th>pred</th>\n",
       "      <th>resid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>131479.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>147327.442043</td>\n",
       "      <td>-15848.442043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>128090.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>138165.739209</td>\n",
       "      <td>-10075.739209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2</td>\n",
       "      <td>147142.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>130036.511738</td>\n",
       "      <td>17105.488262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>107190.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>123367.424927</td>\n",
       "      <td>-16177.424927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>4</td>\n",
       "      <td>106970.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>118486.637902</td>\n",
       "      <td>-11516.637902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  date_block_num  item_cnt_month  month  mth12           pred  \\\n",
       "0 2013-01-01               0        131479.0      1      0  147327.442043   \n",
       "1 2013-02-01               1        128090.0      2      0  138165.739209   \n",
       "2 2013-03-01               2        147142.0      3      0  130036.511738   \n",
       "3 2013-04-01               3        107190.0      4      0  123367.424927   \n",
       "4 2013-05-01               4        106970.0      5      0  118486.637902   \n",
       "\n",
       "          resid  \n",
       "0 -15848.442043  \n",
       "1 -10075.739209  \n",
       "2  17105.488262  \n",
       "3 -16177.424927  \n",
       "4 -11516.637902  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 鲁棒回归模型\n",
    "rlm = smf.rlm('item_cnt_month ~ date_block_num + np.cos((month-6)/12*np.pi) + mth12', data=sales_num_all_tr).fit()\n",
    "sales_num_all_tr['pred'] = rlm.fittedvalues # 预测值\n",
    "sales_num_all_tr['resid'] = rlm.resid # 残差\n",
    "\n",
    "print('总销量鲁棒回归模型RMSE:', mean_squared_error(sales_num_all_tr['item_cnt_month'], sales_num_all_tr['pred']))\n",
    "print('总销量鲁棒回归模型MAE:', mean_absolute_error(sales_num_all_tr['item_cnt_month'], sales_num_all_tr['pred']))\n",
    "print('总销量鲁棒回归模型R方:', r2_score(sales_num_all_tr['item_cnt_month'], sales_num_all_tr['pred']))\n",
    "sales_num_all_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总销量鲁棒回归模型RMSE: 31597999.2593291\n",
      "总销量鲁棒回归模型MAE: 4686.230239922901\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>month</th>\n",
       "      <th>mth12</th>\n",
       "      <th>pred</th>\n",
       "      <th>resid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>32</td>\n",
       "      <td>72843.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>74424.838931</td>\n",
       "      <td>-1581.838931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>33</td>\n",
       "      <td>71056.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>78846.621549</td>\n",
       "      <td>-7790.621549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ymonth  date_block_num  item_cnt_month  month  mth12          pred  \\\n",
       "32 2015-09-01              32         72843.0      9      0  74424.838931   \n",
       "33 2015-10-01              33         71056.0     10      0  78846.621549   \n",
       "\n",
       "          resid  \n",
       "32 -1581.838931  \n",
       "33 -7790.621549  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 验证模型\n",
    "sales_num_all_va['pred'] = rlm.predict(exog=sales_num_all_va)  # 预测值\n",
    "sales_num_all_va['resid'] = sales_num_all_va['item_cnt_month'] - sales_num_all_va['pred'] # 残差\n",
    "\n",
    "print('总销量鲁棒回归模型RMSE:', mean_squared_error(sales_num_all_va['item_cnt_month'], sales_num_all_va['pred']))\n",
    "print('总销量鲁棒回归模型MAE:', mean_absolute_error(sales_num_all_va['item_cnt_month'], sales_num_all_va['pred']))\n",
    "sales_num_all_va.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "sales_num_all_tr_va = pd.concat([sales_num_all_tr, sales_num_all_va], axis=0, ignore_index=True)\n",
    "\n",
    "fig, _ = plt.subplots(figsize=(13, 8))\n",
    "_.remove() # 移除坐标系\n",
    "gs = fig.add_gridspec(nrows=2, ncols=1, height_ratios=[2, 1]) # 划分网格，设置长宽比\n",
    "ax0 = fig.add_subplot(gs[0])\n",
    "ax1 = fig.add_subplot(gs[1])\n",
    "ax0.plot(sales_num_all_tr_va['ymonth'], sales_num_all_tr_va['item_cnt_month'], \n",
    "         color='green', linewidth=2, alpha=0.7, label='true')\n",
    "ax0.plot(sales_num_all_tr_va['ymonth'], sales_num_all_tr_va['pred'], \n",
    "         color='magenta', linewidth=2, alpha=0.7, label='pred')\n",
    "ax1.hlines(y=0, xmin=sales_num_all_tr_va['ymonth'].min(), \n",
    "           xmax=sales_num_all_tr_va['ymonth'].max(), \n",
    "           color='yellow', linewidth=2)\n",
    "ax1.plot(sales_num_all_tr_va['ymonth'], sales_num_all_tr_va['resid'], \n",
    "         color='gray', linewidth=2, alpha=0.7, label='resid')\n",
    "ax0.legend(fontsize=20)\n",
    "ax1.legend(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&emsp;&emsp;拟合结果$R^2$在0.9以上，相当不错，残差也较小。但是任务要求是预测每家店铺，每个商品，每个月的销量，而不是总销量，因此还需要更加细化的模型。这里有2种方式建模，1是对每一家店铺，每一个商品，都建立一个鲁棒回归模型，最后将预测结果组合起来。另一种方式是将店铺编号，商品编号作为模型的分类变量，建立1个模型。考虑到精度和数据量，我们决定使用lighGBM模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM\n",
    "&emsp;&emsp;虽然月份可以作为分类变量进行建模，但是考虑到观测值数量，1家店铺，1个商品只有30多个观测值，显然月份作为分类不太合适，因此我们将月份作为连续变量，由于12月销量暴增，因此将12月份单独提出来作为哑变量。同时将商品类别作为分类变量，商品编号作为分类变量。从EDA的销量历史特征图可以看出，可能存在周期为2个月的季节性特征。因此新增1个2个月滞后特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mth12</th>\n",
       "      <th>item_cnt_month_lag2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ymonth  shop_id  item_id  date_block_num  item_cnt_month  \\\n",
       "0 2014-09-01       54        0              20             1.0   \n",
       "1 2014-04-01       55        1              15             2.0   \n",
       "2 2014-07-01       55        1              18             1.0   \n",
       "3 2014-08-01       55        1              19             1.0   \n",
       "4 2014-09-01       55        1              20             1.0   \n",
       "\n",
       "   item_category_id  month  mth12  item_cnt_month_lag2  \n",
       "0                40      9      0                  0.0  \n",
       "1                76      4      0                  0.0  \n",
       "2                76      7      0                  0.0  \n",
       "3                76      8      0                  0.0  \n",
       "4                76      9      0                  1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义1个函数，添加特征\n",
    "def add_feature_month_and_lag2(df):\n",
    "    df1 = (df.reindex(['ymonth', 'shop_id', 'item_id', 'item_cnt_month'], axis=1)\n",
    "           .set_index('ymonth')\n",
    "           .rename({'item_cnt_month': 'item_cnt_month_lag2'}, axis=1)\n",
    "           .shift(freq='2MS')# 2阶滞后\n",
    "           .reset_index()\n",
    "           .set_index(['ymonth', 'shop_id', 'item_id'])\n",
    "          )\n",
    "    df2 = (df.eval(\"month = ymonth.dt.month\", engine='python')\n",
    "           .pipe(lambda x: x.assign(mth12 = np.where(x['month'] == 12, 1, 0)))\n",
    "           .set_index(['ymonth', 'shop_id', 'item_id'])\n",
    "           .join(df1, how='left')\n",
    "           .reset_index()\n",
    "           .fillna({'item_cnt_month_lag2': 0})\n",
    "          )\n",
    "    \n",
    "    return df2\n",
    "\n",
    "train_valid = add_feature_month_and_lag2(train_df2)\n",
    "train_valid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集\n",
    "train_df3 = train_valid.query(\"ymonth < '2015-10-01'\")\n",
    "# 验证集\n",
    "valid_df3 = train_valid.query(\"ymonth >= '2015-10-01'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ymonth</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mth12</th>\n",
       "      <th>item_cnt_month_lag2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2587</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7687</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12787</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17887</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22987</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID     ymonth  shop_id  item_id  date_block_num  item_category_id  \\\n",
       "0   2587 2015-11-01        5       30              34                40   \n",
       "1   7687 2015-11-01        4       30              34                40   \n",
       "2  12787 2015-11-01        6       30              34                40   \n",
       "3  17887 2015-11-01        3       30              34                40   \n",
       "4  22987 2015-11-01        2       30              34                40   \n",
       "\n",
       "   month  mth12  item_cnt_month_lag2  \n",
       "0     11      0                  0.0  \n",
       "1     11      0                  0.0  \n",
       "2     11      0                  1.0  \n",
       "3     11      0                  0.0  \n",
       "4     11      0                  0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_valid_cut_for_test = (train_valid.query(\"ymonth == '2015-09-01'\")\n",
    "                            .reindex(['shop_id', 'item_id', 'item_cnt_month'], axis=1)\n",
    "                            .rename({'item_cnt_month': 'item_cnt_month_lag2'}, axis=1)\n",
    "                            .set_index(['shop_id', 'item_id'])\n",
    "                           )\n",
    "# 测试集\n",
    "test_df3 = (test_df.assign(date_block_num = 34, ymonth=pd.Timestamp('2015-11-01'))\n",
    "            .set_index('item_id')\n",
    "            .join(items_df.set_index('item_id')[['item_category_id']], how='left')\n",
    "            .reset_index()\n",
    "            .eval(\"month = ymonth.dt.month\", engine='python')\n",
    "            .pipe(lambda x: x.assign(mth12 = np.where(x['month'] == 12, 1, 0)))\n",
    "            .set_index(['shop_id', 'item_id'])\n",
    "            .join(train_valid_cut_for_test, how='left')\n",
    "            .reset_index()\n",
    "            .reindex(['ID', 'ymonth', 'shop_id', 'item_id', 'date_block_num', \n",
    "                      'item_category_id', 'month', 'mth12', 'item_cnt_month_lag2'], axis=1)\n",
    "            .fillna({'item_cnt_month_lag2': 0})\n",
    "           )\n",
    "test_df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py37_for_ml_and_visual\\lib\\site-packages\\lightgbm\\engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "D:\\Anaconda3\\envs\\py37_for_ml_and_visual\\lib\\site-packages\\lightgbm\\basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is ['item_category_id', 'item_id', 'shop_id']\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n",
      "D:\\Anaconda3\\envs\\py37_for_ml_and_visual\\lib\\site-packages\\lightgbm\\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Total Bins 12077\n",
      "[LightGBM] [Info] Number of data points in the train set: 1577593, number of used features: 7\n",
      "[LightGBM] [Info] Start training from score 2.267473\n",
      "Training until validation scores don't improve for 150 rounds"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py37_for_ml_and_visual\\lib\\site-packages\\lightgbm\\basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "D:\\Anaconda3\\envs\\py37_for_ml_and_visual\\lib\\site-packages\\lightgbm\\basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[500]\ttraining's rmse: 4.74392\tvalid_1's rmse: 12.8986\n",
      "[1000]\ttraining's rmse: 4.11102\tvalid_1's rmse: 12.8381\n",
      "[1500]\ttraining's rmse: 3.82128\tvalid_1's rmse: 12.7732\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1500]\ttraining's rmse: 3.82128\tvalid_1's rmse: 12.7732\n",
      "Wall time: 3min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "feature_names = ['date_block_num', 'shop_id', 'item_category_id', 'item_id', 'month', 'mth12', 'item_cnt_month_lag2']\n",
    "cat_features = ['shop_id', 'item_category_id', 'item_id']\n",
    "lgb_train_set = lgb.Dataset(train_df3[feature_names], train_df3['item_cnt_month'])\n",
    "lgb_valid_set = lgb.Dataset(valid_df3[feature_names], valid_df3['item_cnt_month'])\n",
    "\n",
    "params = {'metric': 'rmse', \n",
    "          'num_leaves': 255,\n",
    "          'learning_rate': 0.005, \n",
    "          'feature_fraction': 0.75, \n",
    "          'bagging_fraction': 0.75, \n",
    "          'bagging_freq': 5, \n",
    "          'force_col_wise' : True,\n",
    "          'random_state': 100}\n",
    "\n",
    "lgb_mod = lgb.train(params=params, \n",
    "                    train_set=lgb_train_set, \n",
    "                    valid_sets=(lgb_train_set, lgb_valid_set), \n",
    "                    num_boost_round=1500, # 迭代次数\n",
    "                    early_stopping_rounds=150,\n",
    "                    categorical_feature=cat_features, \n",
    "                    verbose_eval=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "625"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGB模型RMSE: 14.602188168035589\n",
      "LGB模型MAE: 0.9502054435196241\n",
      "LGB模型R方: 0.7974574351636974\n"
     ]
    }
   ],
   "source": [
    "gc.collect(); # 释放内存\n",
    "train_pred = lgb_mod.predict(train_df3[feature_names])\n",
    "print('LGB模型RMSE:', mean_squared_error(train_df3['item_cnt_month'], train_pred))\n",
    "print('LGB模型MAE:', mean_absolute_error(train_df3['item_cnt_month'], train_pred))\n",
    "print('LGB模型R方:', r2_score(train_df3['item_cnt_month'], train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 变量重要性\n",
    "lgb.plot_importance(\n",
    "    lgb_mod, \n",
    "    max_num_features=20, \n",
    "    importance_type='gain', \n",
    "    figsize=(9, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测未来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已保存到文件!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x29e3582b9c8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
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   ],
   "source": [
    "test_result3 = test_df3.copy()\n",
    "test_result3['item_cnt_month'] = lgb_mod.predict(test_df3[feature_names])\n",
    "test_result3.to_csv('./lgb_mod_result.csv', index=False)\n",
    "print('已保存到文件!')\n",
    "\n",
    "# 保存模型\n",
    "lgb_mod.save_model('./lgb_mod.txt')\n",
    "# 重新加载模型\n",
    "#lgb_mod = lgb.Booster(model_file='./lgb_mod.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue; font-size:200%; font-weight:bold\">参考来源:</p>\n",
    "\n",
    "* [lets-plot.org](https://lets-plot.org/pages/api.html)\n",
    "* [joblib.docs](https://joblib.readthedocs.io/en/latest/parallel.html)\n",
    "* [statsmodels.org](https://www.statsmodels.org/stable/tsa.html)\n",
    "* [sklearn.linear_model.Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html)\n",
    "* [sklearn.linear_model.ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html)\n",
    "* [Linear models with sparse coefficients (Lasso)](https://inria.github.io/scikit-learn-mooc/python_scripts/dev_features_importance.html)\n",
    "* [sklearn Polynomial and Spline interpolation](https://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py)\n",
    "* [sklearn Feature importances](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)\n",
    "* [sklearn中线性回归、岭回归和套索回归的参数配置及实例,Lasso,示例](https://www.pythonf.cn/read/126213)\n",
    "* [make_pipeline步骤](https://stackoverflow.com/questions/54646709/sklearn-pipeline-get-feature-names-after-onehotencode-in-columntransformer)\n",
    "* [lightgbm.docs](https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html)\n",
    "* [LightGBM 如何调参](https://www.jianshu.com/p/b4ac0596e5ef)\n",
    "* [工程能力UP！| LightGBM的调参与并行](https://cloud.tencent.com/developer/article/1667226)\n",
    "* [Complete guide on how to Use LightGBM in Python download](https://www.analyticsvidhya.com/blog/2021/08/complete-guide-on-how-to-use-lightgbm-in-python/)\n",
    "* [Python数模笔记-StatsModels 统计回归（2）线性回归](https://www.cnblogs.com/youcans/p/14734197.html)\n",
    "* [Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost](https://machinelearningmastery.com/gradient-boosting-with-scikit-learn-xgboost-lightgbm-and-catboost/)\n",
    "* [Prediction (out of sample)](https://www.statsmodels.org/v0.10.2/examples/notebooks/generated/predict.html)\n",
    "* [Augmented Dickey-Fuller Test in Python](https://www.hackdeploy.com/augmented-dickey-fuller-test-in-python/)\n",
    "* [Feature engineering, LightGBM - Top 1%](https://www.kaggle.com/uladzimirkapeika/feature-engineering-lightgbm-top-1)\n",
    "* [(TOP 3.5%) LightGBM with Feature Engineering](https://www.kaggle.com/werooring/top-3-5-lightgbm-with-feature-engineering)\n",
    "* [Time series Basics : Exploring traditional TS](https://www.kaggle.com/jagangupta/time-series-basics-exploring-traditional-ts)"
   ]
  }
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